Improved p-delta learning algorithm

Authors:
R. Mirsu;V. Tiponut;L. Petromanjanc;Z. Haraszy
Affiliations:
Applied Electronics, "POLITEHNICA" University of Timisoara, Timisoara, Romania;Applied Electronics, "POLITEHNICA" University of Timisoara, Timisoara, Romania;Applied Electronics, "POLITEHNICA" University of Timisoara, Timisoara, Romania;Applied Electronics, "POLITEHNICA" University of Timisoara, Timisoara, Romania
Venue:
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume I
Year:
2010

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Large Margin Classification Using the Perceptron Algorithm

Machine Learning - The Eleventh Annual Conference on computational Learning Theory

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Abstract

This paper presents a modified version of the p-Delta learning algorithm. The algorithm can be used for training a parallel perceptron regardless of the application. There are three significant changes from the original algorithm: an adaptive learning rate, a conscience mechanism and an adaptive margin enhancement mechanism. The changes offer an improved speed, stability and noise margin at the expense of complexity. The higher complexity can be a drawback if the algorithm is intended to be implemented in hardware.